3 citations · 4 across the 12 of their papers we have counts for
12 papers
Imitation Learning via Focused Satisficing
Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski +3
Imitation learning often assumes that demonstrations are close to optimal according to some fixed, but unknown, cost function. However, according to satisficing theory, humans ofte…
Distilling Realizable Students from Unrealizable Teachers
Yujin Kim, Nathaniel Chin, Arnav Vasudev +1
We study policy distillation under privileged information, where a student policy with only partial observations must learn from a teacher with full-state access. A key challenge i…
Efficient Imitation under Misspecification
Nicolas Espinosa-Dice, Sanjiban Choudhury, Wen Sun +1
We consider the problem of imitation learning under misspecification: settings where the learner is fundamentally unable to replicate expert behavior everywhere. This is often true…
Query-Efficient Planning with Language Models
Gonzalo Gonzalez-Pumariega, Wayne Chen, Kushal Kedia +1
Planning in complex environments requires an agent to efficiently query a world model to find a feasible sequence of actions from start to goal. Recent work has shown that Large La…
The Virtues of Pessimism in Inverse Reinforcement Learning
David Wu, Gokul Swamy, J. Andrew Bagnell +2
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a comp…
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
David Wu, Sanjiban Choudhury
Existing inverse reinforcement learning methods (e.g. MaxEntIRL, -IRL) search over candidate reward functions and solve a reinforcement learning problem in the inner loop. This…